Relational databases are able to store, with minimal fuss, pretty much any data entities you throw at them. For the more complex cases – particularly cases involving hierarchical data – they offer many-to-many relationships. Querying many-to-many relationships is usually quite easy: you perform a series of SQL joins in your query; and you retrieve a result set containing the combination of your joined tables, in denormalised form (i.e. with the data from some of your tables being duplicated in the result set).

A denormalised query result is quite adequate, if you plan to process the result set further – as is very often the case, e.g. when the result set is subsequently prepared for output to HTML / XML, or when the result set is used to populate data structures (objects / arrays / dictionaries / etc) in programming memory. But what if you want to export the result set directly to a flat format, such as a single CSV file? In this case, …

Relational databases are able to store, with minimal fuss, pretty much any data entities you throw at them. For the more complex cases – particularly cases involving hierarchical data – they offer many-to-many relationships. Querying many-to-many relationships is usually quite easy: you perform a series of SQL joins in your query; and you retrieve a result set containing the combination of your joined tables, in denormalised form (i.e. with the data from some of your tables being duplicated in the result set).

A denormalised query result is quite adequate, if you plan to process the result set further – as is very often the case, e.g. when the result set is subsequently prepared for output to HTML / XML, or when the result set is used to populate data structures (objects / arrays / dictionaries / etc) in programming memory. But what if you want to export the result set directly to a flat format, such as a single CSV file? In this case, …

I have an interesting problem, on a data migration project I'm currently working on. I'm importing a large amount of legacy data into Drupal, using the awesome Migrate module (and friends). Migrate is a great tool for the job, but one of its limitations is that it requires the legacy database tables to have non-composite integer primary keys. Unfortunately, most of the tables I'm working with have primary keys that are either composite (i.e. the key is a combination of two or more columns), or non-integer (i.e. strings), or both.

Table with composite primary key.

The simplest solution to this problem would be to add an auto-incrementing integer primary key column to the legacy tables. This would provide the primary key information that Migrate needs in order to do its mapping of legacy IDs to Drupal IDs. But this solution has a serious drawback. In my project, …

I have an interesting problem, on a data migration project I'm currently working on. I'm importing a large amount of legacy data into Drupal, using the awesome Migrate module (and friends). Migrate is a great tool for the job, but one of its limitations is that it requires the legacy database tables to have non-composite integer primary keys. Unfortunately, most of the tables I'm working with have primary keys that are either composite (i.e. the key is a combination of two or more columns), or non-integer (i.e. strings), or both.

Table with composite primary key.

The simplest solution to this problem would be to add an auto-incrementing integer primary key column to the legacy tables. This would provide the primary key information that Migrate needs in order to do its mapping of legacy IDs to Drupal IDs. But this solution has a serious drawback. In my project, …